Open Source Text Processing Project: YodaQA

Deep Learning Specialization on Coursera

YodaQA: A Question Answering system built on top of the Apache UIMA framework

Project Website:

Github Link:


YodaQA is an open source Factoid Question Answering system that can produce answer both from databases and text corpora using on-the-fly information extraction. By default, open domain question answering is performed on top of the Freebase and DBpedia knowledge bases as well as the texts of enwiki articles.

YodaQA goals are practicality and extensible design, though it serves as a research project as well. Right now, we are still in early alpha regarding accuracy as well as speed; in the future, we hope to also add some deductive capabilities and include “personal assistant” style conversation capabilities.

YodaQA stands for “Yet anOther Deep Answering pipeline” and the system is built on top of the Apache UIMA and DKpro UIMA bindings and developed as part of the Brmson platform. The QA logic is mostly original work, but much of the designs and componets are inspired by the DeepQA (IBM Watson) and state-of-art papers. See the Acknowledgements section of for more.

The current version is a work-in-progress snapshot that already can answer some questions, even though it’s embarrassingly often wrong; on our reference test set of questions, it can currently choose the correct answer for about 33% of questions (but 46% of questions have the correct answer in top three). Detailed performance info is available at:
More details on YodaQA plus links to some papers are available at:
and you can play with a live demo at
(this demo corresponds to the d/live branch of this git repo).

Also check out our movies QA demo at the d/movies branch and ! (This is actually our primary testbed right now; it answers questions only using databases.)

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